{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Axisymmetric Taylor-Couette flow in Dedalus\n",
    "\n",
    "\n",
    "![Taylor Couette Flow](http://upload.wikimedia.org/wikipedia/commons/3/3d/CouetteTaylorSystem.svg \"Taylor Couette Flow\")\n",
    "(image: wikipedia)\n",
    "\n",
    "Taylor-Couette flow is characterized by three dimensionless numbers:\n",
    "\n",
    "$\\eta = R1/R2$, the ratio of the inner cylidner radius $R_1$ to the outer cylinder radius $R_2$\n",
    "\n",
    "$\\mu = \\Omega_2/\\Omega_1$, the ratio of the OUTER cylinder rotation rate $\\Omega_2$ to the inner rate $\\Omega_1$\n",
    "\n",
    "$\\mathrm{Re} = \\Omega_1 R_1 \\delta/\\nu$, the Reynolds numbers, where $\\delta = R_2 - R_1$, the gap width between the cylinders\n",
    "\n",
    "\n",
    "We non dimensionalize the flow in terms of \n",
    "\n",
    "$[\\mathrm{L}] = \\delta = R_2 - R_1$ \n",
    "\n",
    "$[\\mathrm{V}] = R_1 \\Omega_1$ \n",
    "\n",
    "$[\\mathrm{M}] = \\rho \\delta^3$\n",
    "\n",
    "And choose $\\delta = 1$, $R_1 \\Omega_1 = 1$, and $\\rho = 1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import h5py\n",
    "\n",
    "import dedalus.public as de \n",
    "from dedalus.extras import flow_tools\n",
    "\n",
    "import logging\n",
    "logger = logging.getLogger(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input parameters\n",
    "\n",
    "These parameters are taken from Barenghi (1991) J. Comp. Phys. After running, we'll compare it compute the growth rate and compare it to the value $\\gamma_{analytic} = 0.430108693$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input parameters from Barenghi (1991)\n",
    "eta = 1./1.444 # R1/R2\n",
    "alpha = 3.13   # vertical wavenumber\n",
    "Re = 80.       # in units of R1*Omega1*delta/nu\n",
    "mu = 0.        # Omega2/Omega1\n",
    "\n",
    "# computed quantitites\n",
    "omega_in = 1.\n",
    "omega_out = mu * omega_in\n",
    "r_in = eta/(1. - eta)\n",
    "r_out = 1./(1. - eta)\n",
    "height = 2.*np.pi/alpha \n",
    "v_l = 1. # by default, we set v_l to 1.\n",
    "v_r = omega_out*r_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Domain\n",
    "Every PDE takes place somewhere, so we define a *domain*, which in this case is the $z$ and $r$ directions. Because the $r$ direction has walls, we use a `Chebyshev` basis, but the $z$ direction is periodic, so we use a `Fourier` basis. The `Domain` object combines these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bases\n",
    "r_basis = de.Chebyshev('r', 32, interval=(r_in, r_out), dealias=3/2)\n",
    "z_basis = de.Fourier('z', 32, interval=(0., height), dealias=3/2)\n",
    "domain = de.Domain([z_basis, r_basis], grid_dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equations\n",
    "\n",
    "We use the `IVP` object, which can parse a set of equations in plain text and combine them into an initial value problem.\n",
    "\n",
    "Here, we code up the equations for the \"primative\" variables, $\\mathbf{v} = u \\mathbf{\\hat{r}} + v \\mathbf{\\hat{\\theta}} + w \\mathbf{\\hat{z}}$ and $p$, along with their first derivatives. \n",
    "\n",
    "The equations are the incompressible, axisymmetric Navier-Stokes equations in cylindrical coordinates\n",
    "\n",
    "The axes will be called $z$ and $r$, and we will expand the non-constant $r^2$ terms, to a cutoff precision of $10^{-8}$. These non-constant coefficients (called \"NCC\" in Dedalus) are geometric here, but they could be background states in convection, or position dependent diffusion coefficients, or whatever.\n",
    "\n",
    "We also add the parameters to the object, so we can use their names in the equations below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "TC = de.IVP(domain, variables=['p', 'u', 'v', 'w', 'ur', 'vr', 'wr'], ncc_cutoff=1e-8)\n",
    "TC.parameters['nu'] = 1./Re\n",
    "TC.parameters['v_l'] = v_l\n",
    "TC.parameters['v_r'] = v_r\n",
    "mu = TC.parameters['v_r']/TC.parameters['v_l'] * eta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equations are multiplied through by $r^2$, so that there are no $1/r$ terms, which require more coefficients in the expansion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TC.add_equation(\"r*ur + u + r*dz(w) = 0\")\n",
    "TC.add_equation(\"r*r*dt(u) - r*r*nu*dr(ur) - r*nu*ur - r*r*nu*dz(dz(u)) + nu*u + r*r*dr(p) = -r*r*u*ur - r*r*w*dz(u) + r*v*v\")\n",
    "TC.add_equation(\"r*r*dt(v) - r*r*nu*dr(vr) - r*nu*vr - r*r*nu*dz(dz(v)) + nu*v  = -r*r*u*vr - r*r*w*dz(v) - r*u*v\")\n",
    "TC.add_equation(\"r*dt(w) - r*nu*dr(wr) - nu*wr - r*nu*dz(dz(w)) + r*dz(p) = -r*u*wr - r*w*dz(w)\")\n",
    "TC.add_equation(\"ur - dr(u) = 0\")\n",
    "TC.add_equation(\"vr - dr(v) = 0\")\n",
    "TC.add_equation(\"wr - dr(w) = 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial and Boundary Conditions\n",
    "\n",
    "First we create some aliases to the $r$ and $z$ grids, so we can quickly compute the analytic Couette flow solution for unperturbed, unstable axisymmetric flow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = domain.grid(1, scales=domain.dealias)\n",
    "z = domain.grid(0, scales=domain.dealias)\n",
    " \n",
    "p_analytic = (eta/(1-eta**2))**2 * (-1./(2*r**2*(1-eta)**2) -2*np.log(r) +0.5*r**2 * (1.-eta)**2)\n",
    "v_analytic = eta/(1-eta**2) * ((1. - mu)/(r*(1-eta)) - r * (1.-eta) * (1 - mu/eta**2)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we add boundary conditions, simply by typing them in plain text, just like the equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boundary conditions\n",
    "TC.add_bc(\"left(u) = 0\")\n",
    "TC.add_bc(\"left(v) = v_l\")\n",
    "TC.add_bc(\"left(w) = 0\")\n",
    "TC.add_bc(\"right(u) = 0\", condition=\"nz != 0\")\n",
    "TC.add_bc(\"right(v) = v_r\")\n",
    "TC.add_bc(\"right(w) = 0\")\n",
    "TC.add_bc(\"left(p) = 0\", condition=\"nz == 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now set the parameters of the problem, $\\nu$, $v_l$, and $v_r$, and have the code log $\\mu$ to the output (which can be `stdout`, a file, or both)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timestepping\n",
    "\n",
    "We have implemented a variety of multi-step and Runge-Kutta implicit-explicit timesteppers in Dedalus.  The available options can be seen in the [timesteppers.py module](https://github.com/DedalusProject/dedalus/blob/master/dedalus/core/timesteppers.py).  Here we pick `RK443`, an IMEX Runga-Kutta scheme. We set our maximum timestep `max_dt`, and choose the various stopping parameters.\n",
    "\n",
    "Finally, we've got our full initial value problem (represented by the `IVP`) object: a timestepper, a domain, and a `ParsedProblem` (or equation set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "dt = max_dt = 1.\n",
    "omega1 = TC.parameters['v_l']/r_in\n",
    "period = 2*np.pi/omega1\n",
    "\n",
    "ts = de.timesteppers.RK443\n",
    "IVP = TC.build_solver(ts)\n",
    "IVP.stop_sim_time = 15.*period\n",
    "IVP.stop_wall_time = np.inf\n",
    "IVP.stop_iteration = 10000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initialize the state vector, given by `IVP.state`. To make life a little easier, we set some aliases first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = IVP.state['p']\n",
    "u = IVP.state['u']\n",
    "v = IVP.state['v']\n",
    "w = IVP.state['w']\n",
    "ur = IVP.state['ur']\n",
    "vr = IVP.state['vr']\n",
    "wr = IVP.state['wr']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a new field, $\\phi$, defined on the `domain`, which we'll use below to compute incompressible, random velocity perturbations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi = domain.new_field(name='phi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we set all these to their dealiased domains. Dedalus allows you to set the \"scale\" of your data: this allows you to interpolate your data to a grid of any size at spectral accuracy. Of course, this isn't CSI: Fluid Dynamics, so you won't get any more detail than your highest spectral coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in [phi,p,u,v,w,ur,vr,wr]:\n",
    "    f.set_scales(domain.dealias, keep_data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we set the state vector with our previously computed analytic solution, compute the first derivatives (to make our system first order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Field 140589376849848>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v['g'] = v_analytic\n",
    "#p['g'] = p_analytic\n",
    "\n",
    "v.differentiate('r',out=vr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, we set some small, incompressible perturbations to the velocity field so we can kick off our linear instability.\n",
    "\n",
    "First, we initialize $\\phi$ (which we created above) to Gaussian noise and then mask it to only appear in the center of the domain, so we don't violate the boundary conditions. We then take its curl to get the velocity perturbations.\n",
    "\n",
    "Unfortunately, Gaussian noise on the grid is generally a bad idea: zone-to-zone variations (that is, the highest frequency components) will be amplified arbitrarily by any differentiation. So, let's filter out those high frequency components using this handy little function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_field(field,frac=0.5):\n",
    "    dom = field.domain\n",
    "    local_slice = dom.dist.coeff_layout.slices(scales=dom.dealias)\n",
    "    coeff = []\n",
    "    for i in range(dom.dim)[::-1]:\n",
    "        coeff.append(np.linspace(0,1,dom.global_coeff_shape[i],endpoint=False))\n",
    "    cc = np.meshgrid(*coeff)\n",
    "    \n",
    "    field_filter = np.zeros(dom.local_coeff_shape,dtype='bool')\n",
    "    for i in range(dom.dim):\n",
    "        field_filter = field_filter | (cc[i][local_slice] > frac)\n",
    "    field['c'][field_filter] = 0j"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not the best filter: it assumes that cutting off above a certain Chebyshev mode $n$ and Fourier mode $n$ will be OK, though this may produce anisotropies in the data (I haven't checked). If you're worrying about the anisotropy of the initial noise of your ICs, you can always replace this filter with something better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Field 140589376849904>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# incompressible perturbation, arbitrary vorticity\n",
    "# u = -dz(phi)\n",
    "# w = dr(phi) + phi/r\n",
    "\n",
    "phi['g'] = 1e-3* np.random.randn(*v['g'].shape)*np.sin(np.pi*(r - r_in))\n",
    "filter_field(phi)\n",
    "phi.differentiate('z',out=u)\n",
    "u['g'] *= -1\n",
    "phi.differentiate('r',out=w)\n",
    "w['g'] += phi['g']/r\n",
    "\n",
    "u.differentiate('r',out=ur)\n",
    "w.differentiate('r',out=wr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we check that incompressibility is indeed satisfied, first by computing $\\nabla \\cdot u$,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "divu0 = domain.new_field(name='divu0')\n",
    "u.differentiate('r',out=divu0)\n",
    "divu0['g'] += u['g']/r + w.differentiate('z')['g']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then by plotting it to make sure it's nowhere greater than $\\sim 10^{-15}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kburns/Software/miniconda3/envs/dedalus/lib/python3.6/site-packages/ipykernel_launcher.py:2: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'z')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12,8)\n",
    "pcolormesh((r[0]*ones_like(z)).T,(z*ones_like(r)).T,divu0['g'].T,cmap='PuOr')\n",
    "colorbar()\n",
    "axis('image')\n",
    "xlabel('r', fontsize=18)\n",
    "ylabel('z', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time step size and the CFL condition\n",
    "\n",
    "Here, we use the nice CFL calculator provided by the `flow_tools` package in the `extras` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "CFL = flow_tools.CFL(IVP, initial_dt=1e-3, cadence=5, safety=0.3,\n",
    "                     max_change=1.5, min_change=0.5)\n",
    "CFL.add_velocities(('u', 'w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "Dedalus has a very powerful inline analysis system, and here we set up a few."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integrated energy every 10 iterations\n",
    "analysis1 = IVP.evaluator.add_file_handler(\"scalar_data\", iter=10)\n",
    "analysis1.add_task(\"integ(0.5 * (u*u + v*v + w*w))\", name=\"total kinetic energy\")\n",
    "analysis1.add_task(\"integ(0.5 * (u*u + w*w))\", name=\"meridional kinetic energy\")\n",
    "analysis1.add_task(\"integ((u*u)**0.5)\", name='u_rms')\n",
    "analysis1.add_task(\"integ((w*w)**0.5)\", name='w_rms')\n",
    "\n",
    "# Snapshots every half an inner rotation period\n",
    "analysis2 = IVP.evaluator.add_file_handler('snapshots',sim_dt=0.5*period, max_size=2**30)\n",
    "analysis2.add_system(IVP.state, layout='g')\n",
    "\n",
    "# radial profiles every 100 timestpes\n",
    "analysis3 = IVP.evaluator.add_file_handler(\"radial_profiles\", iter=100)\n",
    "analysis3.add_task(\"integ(r*v, 'z')\", name='Angular Momentum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Main Loop\n",
    "And here we actually run the simulation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-30 21:57:06,891 __main__ 0/1 INFO :: Iteration: 10, Time: 1.200000e-02, dt: 1.500000e-03\n",
      "2020-10-30 21:57:07,550 __main__ 0/1 INFO :: Iteration: 20, Time: 3.825000e-02, dt: 3.375000e-03\n",
      "2020-10-30 21:57:07,965 __main__ 0/1 INFO :: Iteration: 30, Time: 9.731250e-02, dt: 7.593750e-03\n",
      "2020-10-30 21:57:08,373 __main__ 0/1 INFO :: Iteration: 40, Time: 2.302031e-01, dt: 1.708594e-02\n",
      "2020-10-30 21:57:09,029 __main__ 0/1 INFO :: Iteration: 50, Time: 5.292070e-01, dt: 3.844336e-02\n",
      "2020-10-30 21:57:09,662 __main__ 0/1 INFO :: Iteration: 60, Time: 1.201966e+00, dt: 8.649756e-02\n",
      "2020-10-30 21:57:10,245 __main__ 0/1 INFO :: Iteration: 70, Time: 2.715673e+00, dt: 1.946195e-01\n",
      "2020-10-30 21:57:10,643 __main__ 0/1 INFO :: Iteration: 80, Time: 6.121514e+00, dt: 4.378939e-01\n",
      "2020-10-30 21:57:11,072 __main__ 0/1 INFO :: Iteration: 90, Time: 1.378466e+01, dt: 9.852613e-01\n",
      "2020-10-30 21:57:11,596 __main__ 0/1 INFO :: Iteration: 100, Time: 3.102673e+01, dt: 2.216838e+00\n",
      "2020-10-30 21:57:12,092 __main__ 0/1 INFO :: Iteration: 110, Time: 6.982139e+01, dt: 4.987885e+00\n",
      "2020-10-30 21:57:12,705 __main__ 0/1 INFO :: Iteration: 120, Time: 1.571094e+02, dt: 1.122274e+01\n",
      "2020-10-30 21:57:12,985 solvers 0/1 INFO :: Simulation stop time reached.\n",
      "2020-10-30 21:57:12,994 __main__ 0/1 INFO :: Total time: 6.586734\n",
      "2020-10-30 21:57:12,997 __main__ 0/1 INFO :: Iterations: 124\n",
      "2020-10-30 21:57:13,017 __main__ 0/1 INFO :: Average timestep: 1.760728\n"
     ]
    }
   ],
   "source": [
    "dt = CFL.compute_dt()\n",
    "# Main loop\n",
    "start_time = time.time()\n",
    "\n",
    "while IVP.ok:\n",
    "    IVP.step(dt)\n",
    "    if IVP.iteration % 10 == 0:\n",
    "        logger.info('Iteration: %i, Time: %e, dt: %e' %(IVP.iteration, IVP.sim_time, dt))\n",
    "    dt = CFL.compute_dt()\n",
    "\n",
    "\n",
    "end_time = time.time()\n",
    "\n",
    "# Print statistics\n",
    "logger.info('Total time: %f' %(end_time-start_time))\n",
    "logger.info('Iterations: %i' %IVP.iteration)\n",
    "logger.info('Average timestep: %f' %(IVP.sim_time/IVP.iteration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "First, let's look at our last time snapshot, plotting the background $v \\mathbf{\\hat{\\theta}}$ velocity with arrows representing the meridional flow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kburns/Software/miniconda3/envs/dedalus/lib/python3.6/site-packages/ipykernel_launcher.py:2: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'z')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12,8)\n",
    "pcolormesh((r[0]*ones_like(z)).T,(z*ones_like(r)).T,v['g'].T,cmap='PuOr')\n",
    "quiver((r[0]*ones_like(z)).T,(z*ones_like(r)).T,u['g'].T,w['g'].T,width=0.005)\n",
    "axis('image')\n",
    "xlabel('r', fontsize=18)\n",
    "ylabel('z', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we really want some quantitative comparison with the growth rate $\\gamma_{analytic}$ from Barenghi (1991). First we construct a small helper function to read our timeseries data, and then we load it out of the self-documented HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_timeseries(data, field):\n",
    "    data_1d = []\n",
    "    time = data['scales/sim_time'][:]\n",
    "    data_out = data['tasks/%s'%field][:,0,0]\n",
    "    return time, data_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = h5py.File(\"scalar_data/scalar_data_s1/scalar_data_s1_p0.h5\", \"r\")\n",
    "t, ke = get_timeseries(data, 'total kinetic energy')\n",
    "t, kem = get_timeseries(data, 'meridional kinetic energy')\n",
    "t, urms = get_timeseries(data, 'u_rms')\n",
    "t, wrms = get_timeseries(data, 'w_rms')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compare to Barenghi (1991), we scale our results by the Reynolds number, because we have non-dimensionalized slightly differently than he did. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma_w = 0.34979841\n",
      "relative error = 1.86720895e-01\n"
     ]
    }
   ],
   "source": [
    "t_window = (t/period > 2) & (t/period < 14)\n",
    "\n",
    "gamma_w, log_w0 = np.polyfit(t[t_window], np.log(wrms[t_window]),1)\n",
    "\n",
    "gamma_w_scaled = gamma_w*Re\n",
    "gamma_barenghi = 0.430108693\n",
    "rel_error_barenghi = (gamma_barenghi - gamma_w_scaled)/gamma_barenghi\n",
    "\n",
    "print(\"gamma_w = %10.8f\" % gamma_w_scaled)\n",
    "print(\"relative error = %10.8e\" % rel_error_barenghi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like a rather high error (order 10% or so), but we know from Barenghi (1991) that the error is dominated by the timestep. Here, we've used a very loose timestep, but if you fix dt (rather than using the CFL calculator), you can get much lower errors at the cost of a longer run.\n",
    "\n",
    "Finally, we plot the RMS $w$ velocity, and compare it with the fitted exponential solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHeUP4ZmZmfruu+/0/vvvt2rKv+ZUV1e32Kt7oAEDBjQ5vnno0KFavXq19u7d26B3uqioSNHR0c32gh/KOVrbfu/evbrhhht09tlna/Dgwfr222/9bSSptLRU3377raKjo1v8ZiE+Pl4TJkzQX/7yl0ZDc1VVlZ599ln1799f06ZNa/Zcd955p+69915deeWVWrBgQbNtO1pIjQkHAAA4FGPGjNE333yjoqIi3X///frNb36jSZMm+ffHxsbqs88+k1Q7N7UxRtnZ2ZKkkpIS9evXzx84D/Tjjz9qyJAhrf758ccfm6zxuOOOU01Njb+O/SorK7Vy5UolJCS0eJ9tPUdr2+/Zs0fbtm3TkiVLNHr0aP/P/uFLubm5Gj16tP/PrCV79uxp8oHWxYsXa8uWLUpJSWlyqIwk3XXXXbrrrrt02WWXKTs7W8aYVl27o9ATDgAA0IIxY8Zo9+7dSklJUU1NjR5++OF6+6OiorRr1y5J0sMPP6xx48b5p9BbuHChpk6dqpiYmAbn7cgx4RdddJHuv/9+Pfroo/UeWvzb3/6miooKXXrppfXa+3w+eb1eud1uDRs27JDO0dr2hx12mF566aUGNW/btk1paWmaPHmypk+frvj4eP++zZs3N3q/S5cu1VdffdXk8wf7h6JMnz690f2SdPfdd+vOO+9USkqKnnrqqXZ/o3EoCOEAAAAt2P+w3tKlS3XnnXf6Q+t+/fr1U1lZmQoLC/XRRx9pzpw52rZtm6qqqvSXv/xFr732WqPn7cgx4ePHj9c111yjefPm6YILLtDZZ5/tX73ylFNOaTDrR1FRkcaNG6dTTjnFP4ynredobXuXy9XoQ7fr16+XVPsM3cH7Z8+erU2bNul//ud/NHz4cFVWVmr58uVatGiR+vbtq8zMzAbn27hxo9566y0df/zxGj9+fKN/TvPnz9cdd9yhYcOG6fTTT9fzzz9fb/+gQYN0xhlnNP0H3UEI4QAAAC3YH8KHDRum3/3udw327+8Jf/TRRzV79mxFR0dr7dq1+sc//qHRo0drwoQJjtT56KOPasSIEVq4cKGWLFmi6OhozZkzR3fffXere3vbeo6OuGZjLrnkEj3zzDPKycnRtm3bZIzR8OHDNXPmTN10000N/iEkSU8//bSqq6ubfSBz/9zlP/zwgy6//PIG+0855RRHQripXVEztCQkJNhly5YFuwwAANBFeL1ejRo1Svfee69uvfXWBvsvu+wyjRkzRn/961+1atUqvf/++3r++ee1YcMG3XbbbS0uLIPuyRiz3Frb6GB8HswEAABowcqVKyVJEydObHR/VFSU5s+fr4suukiRkZGKjIzUBx98oNLSUiUmJjpYKboKQjgAAEAL9ofwpoaVREVFadu2bbruuuskSZGRkdq0aZPS09Mdn3UDXQPDUQAAAFpwzjnnaNmyZdq8eXOwS0EX0txwFB7MBAAAaMEbb7wR7BLQzTAcBQAAAHAYIRwAAABwWEiFcGPMVGPMwtLS0mCXAgAAgBAWUiHcWrvYWpsaGRkZ7FIAAAAQwkIqhAMAAACdASEcAAAAcBghHAAAAHAYIRwAAABwGCEcAAAAcBghHAAAAHAYIRwAAABwGCE8wLxer9LS0uTxeBQWFiaPx6O0tDR5vd5glwYAAIAgIYQHUH5+vuLj45Wdna2ysjJZa1VWVqbs7GzFx8crPz8/2CUCAAAgCAjhAeL1epWUlKSKigr5fL56+3w+nyoqKpSUlESPOAAAQAgihAdIZmZmg/B9MJ/Pp6ysLIcqAgAAQGdBCA+Q3NzcVoXwnJwchyoCAABAZ0EID5Dy8vIObQcAAIDugxAeIBERER3aDgAAAN0HITxAkpOT5XK5mm3jcrmUkpLiUEUAAADoLEIqhBtjphpjFpaWlgb8WhkZGa0K4enp6QGvBQAAAJ1LSIVwa+1ia21qZGRkwK8VFxenvLw8ud3uBmHc5XLJ7XYrLy9PcXFxAa8FAAAAnUtIhXCnJSYmqqCgQKmpqfVWzExNTVVBQYESExODXSIAAACCwFhrg12D4xISEuyyZcuCXQYAAAC6MWPMcmttQmP76AkHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABwWUiHcGDPVGLOwtLQ02KUAAAAghIVUCLfWLrbWpkZGRga7FAAAAISwkArhAAAAQGdACAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCHeA1+tVWlqaPB6PwsLC5PF4lJaWJq/XG+zSAAAAEASE8ADLz89XfHy8srOzVVZWJmutysrKlJ2drfj4eOXn5we7RAAAADiMEB5AXq9XSUlJqqiokM/nq7fP5/OpoqJCSUlJ9IgDAACEGEJ4AGVmZjYI3wfz+XzKyspyqCIAAAB0BoTwAMrNzW1VCM/JyXGoIgAAAHQGhPAAKi8v79B2AAAA6B4I4QEUERHRoe0AAADQPYRUCDfGTDXGLCwtLXXkesnJyXK5XM22cblcSklJcaQeAAAAdA4hFcKttYuttamRkZGOXC8jI6NVITw9Pd2RegAAANA5hFQId1pcXJzy8vLkdrsbhHGXyyW32628vDzFxcUFqUIAAAAEAyE8wBITE1VQUKDU1NR6K2ampqaqoKBAiYmJwS4RAAAADjPW2mDX4LiEhAS7bNmyYJcBAACAbswYs9xam9DYPnrCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAh4VUCDfGTDXGLCwtLQ12KQAAAAhhIRXCrbWLrbWpkZGRwS4FAAAAISykQjgAAADQGRDCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcId4PV6lZaWJo/Ho7CwMHk8HqWlpcnr9Qa7NAAAAAQBITzA8vPzFR8fr+zsbJWVlclaq7KyMmVnZys+Pl75+fnBLhEAAAAOI4QHkNfrVVJSkioqKuTz+ert8/l8qqioUFJSEj3iAAAAIYYQHkCZmZkNwvfBfD6fsrKyHKoIAAAAnQEhPIByc3NbFcJzcnIcqggAAACdASE8gMrLyzu0HQAAALqHdoVwY8xRxhh3RxXT3URERHRoOwAAAHQP7e0Jf1nS0ZJkjBlvjFlsjLnDGNOj/aV1fcnJyXK5XM22cblcSklJcagiAAAAdAbtDeHDJa2oe32fpBJJv5B0TzvP2y1kZGS0KoSnp6c7VBEAAAA6g/aG8N2SwowxEaoN36mSkiX9b3sL6w7i4uKUl5cnt9vdIIy7XC653W7l5eUpLi4uSBUCAAAgGNobwpdKuk3SLZI+sdbulbRd0uHtLay7SExMVEFBgVJTU+utmJmamqqCggIlJiYGu0QAAAA4zFhrD/1gYwZJekLSaEkp1trPjDFjJf3TWjusg2rscAkJCXbZsmXBLgMAAADdmDFmubU2obF94e05sbV2i6RzDto8TtIL7TkvAAAA0J21K4Q3xlr7iqRXOvq8AAAAQHfRrhBujOmt2p7wcEmrJa2y1lZ3RGEAAABAd9XenvDFksZK+lG184X3NMb8V9IKa+3s9hYHAAAAdEftDeE/lzTMWrtTkowxcZImSprQzvMCAAAA3VZ7Q3iBJP8E2NZarySvpJfaeV4AAACg22rvPOHXSvo/Y0xkRxQDAAAAhIL29oRPkHSmpI3GmA8lLZO0XNJya+2G9hYHAAAAdEftDeF/kvRHSV9KOka1ofxW1T6k2aed5wYAAAC6pfaG8ApJC6y1NZI+2r/RGNPh848DAAAA3UV7x4Q/Lumigzdaa6vaeV4AAACg22pvj/XFkkYaY46X9Lpq5wcvbX9ZAAAAQPfV3p7wmyU9JGmkpKcllRhjvMYYpigEAAAAmnDIPeHGmB6SrpF0vrV2b922wyX9TNKxHVIdAAAA0A0dcgi31lYbY8ZLqjlg23ZJ/6z7AQAAANCI9g5HeULS1R1RCAAAABAq2vtg5oWSRhtjEiT9XdLnPJgJAAAANK8jHsy8T9Lhqu0V58FMAAAAoAWt6gk3xtwhaV7dmG8/a+0bkt44oB0PZgIAAAAtaG1P+K2SfjTGLDDGHNVUI2vtdmvtP621f+qY8gAAAIDup7UhfKSkR1U7Bny1MeZ1Y8ykgFUFAAAAdGOtCuHW2iJr7R8kxUqaJWm4pPeMMcuMMRfXzRkOAAAAoBXa9GCmtXavtTbbWnuMpNMkFUrKkfSdMeaGQBQIAAAAdDetfTBzmKQoSf3rfu9/vVqSW9Lpkh6W9EhgygQAAAC6j9bOE/79Aa+NpL2SSiXtqvu9tO43AAAAgBa0NoTvltRL0pOS7rPWFgauJAAAAKB7a+2Y8FhJt0uaKmmtMebx5qYqBAAAANC01s6Ossta+5Bqpyq8RtKJqp2q8DVjzEmBLLAlxphxdfOX5xljZgezFgAAAKA12jo7is9a+5S1dryk8yT1lfSBMeYTY8yFbb24MeZJY8xWY8xXB22fbIz5xhjzrTHm5hZq+tpaO0vSbyQltLUGAAAAwGmtnR1llGoDt+eg32+q9iHNsyS90NrzHeBpSfMkPXvAtXpImi/pDNVOgfi5MeZ1ST0kPXDQ8VdZa7caY86VdHPduQAAAIBOrbWhea0kW/faHLSvUtImSTvaenFr7QfGmBEHbT5e0rfW2u8kyRizSNJ51toHJJ3TxHlel/S6MWaJpOfbWgcAAADgpNaG8HRJJaoN2gf+lFhr93ZwTTGSfjzgfaGknzfV2BjzK0kXqHb2ljebaZcqKVWShg0b1gFlAgAAAIemVSHcWvtYoAs5wME97dL/64VvuMPa9yS919JJrbULJS2UpISEhCbPBwAAAARamx7MdEihpCMOeB8raWOQagEAAAA6XGcM4Z9LGm2MGWmM6SnpYkmvB7kmAAAAoMMENYQbY/4u6RNJY4wxhcaY6dbaKkm/lfS2pK8lvWitXRXMOgEAAICO1NYpBTuUtfaSJra/qWYesgQAAAC6ss44HAUAAADo1gjhAAAAgMMI4QAAAIDDCOEAAACAw0IqhBtjphpjFpaWlga7FAAAAISwkArh1trF1trUyMjIYJcCAACAEBZSIRwAAADoDAjhAAAAgMMI4QAAAIDDCOEAAACAwwjhAAAAgMMI4QAAAIDDCOEAAACAwwjhAAAAgMMI4QAAAIDDQiqEs2w9AAAAOoOQCuEsWw8AAIDOIKRCOAAAANAZEMIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwgEAAACHEcIBAAAAhxHCAQAAAIcRwh3i9XqVlpYmj8ejsLAweTwepaWlyev1Brs0AAAAOMxYa4Ndg2OMMVMlTR01atSMdevWOXbd/Px8JSUlyefzyefz+be7XC65XC7l5eUpMTHRsXoAAAAQeMaY5dbahEb3hVII3y8hIcEuW7bMkWt5vV7Fx8eroqKiyTZut1sFBQWKi4tzpCYAAAAEXnMhnOEoAZaZmVmv97sxPp9PWVlZDlUEAACAYCOEB1hubm6rQnhOTo5DFQEAACDYCOEBVl5e3qHtAAAA0PURwgMsIiKiQ9sBAACg6yOEB1hycrJcLlezbVwul1JSUhyqCAAAAMFGCA+wjIyMVoXw9PR0hyoCAABAsBHCAywuLk55eXlyu90NwrjL5ZLb7VZeXh7TEwIAAIQQQrgDEhMTVVBQoNTU1HorZqampqqgoICFegAAAEIMi/UAAAAAAcBiPQAAAEAnQggHAAAAHEYIBwAAABxGCAcAAAAcFlIh3Bgz1RizsLS0NNilAAAAIISFVAi31i621qZGRkYGuxQAAACEsJAK4QAAAEBnQAgHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABwWUiHcGDPVGLOwtLQ02KUAAAAghIVUCLfWLrbWpkZGRga7FAAAAISwkArhAAAAQGdACAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcFlIh3Bgz1RizsLS0NNilAAAAIISFVAi31i621qZGRkYGuxQAAACEsJAK4QAAAEBnQAgHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggPAq/Xq7S0NHk8HoWFhcnj8SgtLU1erzfYpQEAAMABhHCH5efnKz4+XtnZ2SorK5O1VmVlZcrOzlZ8fLzy8/ODXSIAAAACjBDuIK/Xq6SkJFVUVMjn89Xb5/P5VFFRoaSkJHrEAQAAujlCuIMyMzMbhO+D+Xw+ZWVlOVQRAAAAgoEQ7qDc3NxWhfCcnByHKgIAAEAwEMIdVF5e3qHtAAAA0DURwh0UERHRoe0AAADQNRHCHZScnCyXy9VsG5fLpZSUFIcqAgAAQDAQwh2UkZHRqhCenp7uUEUAAAAIBkK4g+Li4pSXlye3290gjLtcLrndbuXl5SkuLi5IFQIAAMAJhHCHJSYmqqCgQKmpqfVWzExNTVVBQYESExODXSIAAAACzFhrg12D4xISEuyyZcuCXQYAAAC6MWPMcmttQmP76AkHAAAAHEYIBwAAQLfj9XqVlpZWb/hvWlqavF5vsEuTRAgHAABAN5Ofn6/4+HhlZ2errKxM1lqVlZUpOztb8fHxys/PD3aJhHAAAAB0H16vV0lJSaqoqJDP56u3z+fzqaKiQklJSUHvESeEAwAAoNvIzMxsEL4P5vP5lJWV5VBFjSOEAwAAoEvbu3evvv/+e/373//WU0891aoQnpOT41B1jQsP6tUdZoyZKmnqqFGjgl0KAAAAWmCtVWlpqYqKilRVVaVjjjlGknTTTTcpLi5Os2bNUlVVlQ477DBVV1e36dzl5eWBKLnVQiqEW2sXS1qckJAwI9i1AAAAhDJrrYwxkqR33nlHa9euVWFhoYqKivy/i4qKtHv3bknSiSeeqI8++kiStGzZMlVVVUmSwsPD9dBDDykqKkqxsbGaNm2aKioqWrx+REREgO6sdUIqhAMAACDwKisrG4TpsrIy3X333ZKkyy+/XGvWrNGnn34qSfrjH/+ozz77TOHh4YqJiVFMTIyOOeYYTZkyxf8+Li7Of/6lS5fWu15GRob/9eWXX67s7Oxmh6S4XC6lpKR05C23GStmAgAAoE0KCwu1Zs0anX766ZKkxx9/XK+99po/eJeUlDQ4JioqSsXFxQoLC9PTTz+tzZs36+abb5YkfffddzrssMM0YMAAhYW175FFr9er+Pj4ZnvD3W63CgoK6gX7QGhuxUx6wgEAAKDq6mpt3ry5QQ/2ga/fe+89DR06VM8884z++Mc/as+ePerdu7c2bdqkLVu2aPjw4TrppJP8vdexsbH+1x6Px3+tK664ot61jzzyyA67j7i4OOXl5SkpKUk+n69ej7jL5ZLL5VJeXl7AA3hL6AkHAADo5vbs2aOioiINGDBAkZGRWrVqlR5//HH9/ve/V0xMjB5//HGlpaWppqam3nEul8sfomNiYvTwww9r2LBhWr9+vYqKinT88cfL5XIF6a6a5/V6lZWVpZycHJWXlysiIkIpKSlKT093LIA31xNOCAcAAOjidu7cqY8//rjR3uvCwkLt2LFDkvTiiy/q17/+tf71r39p2rRpevvtt3XCCSfos88+0+LFixv0YEdHR7d7eEgoI4QfhBAOAAA6u6qqqgbDQ4499lhNmjRJGzdu1KRJk3TPPffokksu0SeffKITTzxRkmSM0aBBgxodEvKrX/1Kw4cPrzczCQKHMeEAAACdzLp169SzZ08NHz5cu3fv1s0331yvB3vz5s0NhodkZGRo0qRJioqK0nHHHaeBAwdKksaPH6+PPvpIsbGxGjJkSItDRAjgwUdPOAAAQAex1mr79u31eq8P/P3Tn/5UmZmZkqQBAwYoKSlJf/3rX1VdXa1BgwZp8ODB9XqvD/59+OGHE6C7EHrCAQAA2snn86mkpESDBg2SJC1atEi7d+/W9OnTJUmTJk3SZ599pr1799Y7zhijwYMHKzY2Vj179vRvf/LJJzVs2DBJUo8ePVRcXOzQnaAzIIQDAABI2rBhg7xeb6M92EVFRdqyZYuGDBmioqIiSdLzzz+vjRs3+kP4qaeeqhNOOKHBOOzBgwc3Ojxk6tSpjt4fOheGowAAgG6rpqZGxcXF9WYKmT59ulwul+bNm6fnn39eH3/8sSTpoosu0osvvug/dv8y6Ac+4Dhs2DBdddVVkuSfI5vhIWgKw1EAAEC34/P5tHHjRg0dOlQul0v//ve/9eqrr9brvd64caP27dtX77izzz5bw4YNk8fj0eDBg1VdXa0ePXro97//vWbPnq3Y2FgNHTpUbre72ev36dMnkLeHbo6ecAAA0Ons2rWryTmv7777bh177LF67rnnlJycrDVr1mjMmDF67LHHdMsttzQYDnLw6yFDhjD3NRxBTzgAAOgUampqVFVVpZ49e6qkpEQvvPCCTj/9dI0ePVoffvihZsyYoaKiIpWVlTU4tn///oqNjdWuXbskSSeeeKL+9re/KTo6WpJ0zTXX6Nprr2V4CLoEQjgAAOgQ1dXV+uGHH5rswd4/POS+++7TTTfdpJ07dyotLU1PPvmkRo8erf79++snP/mJzjzzzAa910OHDm0w/GPkyJG6+uqr/e/Dw4k16Dr4rxUAALRo165d/iDt8Xj085//XNZanXfeeZo6dapmzJihrVu36sgjj6x3nNvt9gfpX/7yl4qJidHPf/5zSdLw4cO1ceNG/4IzRx99tPLy8hy/NyAYCOEAAISw6upqVVRUqG/fvpKkv/3tb/r+++8b9GCXl5f7j5k2bZpefvllGWNUXl6uyspKSdLAgQOVnZ1db4GZyMjIJoeH9OjRQ0OGDAn8TQKdEA9mAgDQTVVWVmrjxo0N5rzu1auXHnroIUnSCSecoH79+umtt96SVDvEo7CwUEOGDGly1caRI0cqNjY2mLcGdAk8mAkAQDdirfX3Lq9cuVJer1cXXnihJOmWW25Rfn6+ioqKGl2B8bDDDtPEiRP976+55pp6qzguX75c/fr1Y/YQIMAI4QAAdCLV1dXasmVLg+EgB/7etGmTiouL1atXLz355JN65pln/CE8LCxMRxxxhE444YQGPdgxMTHyeDz1hoekpKTUu37//v0dvV8gVDEcBQAAh+zZs8cfridMmCCPx6OlS5dq/vz5euqpp9S3b1/dfPPN/qEi+7lcLg0dOrReoL7rrrvUt29fFRUVad++fRoxYgRT8wGdDMNRAAAIIGutrLUKCwvThg0b9M477zQ6PV9JSYn/mPfff1+TJk3Sjh07tGrVKu3YsUN9+/bV+eefr+HDh9frvR4wYECTw0NiYmKcuk0AHYiecAAAmlFVVaUtW7Y0CNXnnnuuTjrpJC1fvly//OUv9corr+iss87Sa6+9pvPPP1/GGA0cOLDJ1RsTEhLUr1+/YN8egACiJxwAgCZUVVXpww8/VGxsrEaNGqWioiLNmTPHH7Y3b96smpqaesf07NlTw4cP10knnaTY2FjNnj3b3yN92mmnaf369RoyZEi9Bx4B4ED0hAMAuh1rrUpKShodErL/9ZQpU/Tggw/K5/OpV69euu2223TXXXepuLhYp5xySpM92DExMYqOjmb2EAAtoiccANBtVFVVafPmzdq9e7fGjBkjSXrggQfUv39/zZw5U9Za9e/fXzt37qx3nDFGgwYNUkxMjI488kiNHDlSUu1Dj++9955GjRolSYqOjtaqVascvScAoYcQDgDodD799FOtXbu20R7sLVu2qKamRhMmTNCKFSskSW+//baGDRummTNnyhijm266qd5y6TExMRoyZIhcLlej15s0aZKTtwcADEcBAASetVbFxcX1QvX27dv1hz/8QZKUnp6ujz/+WJ9++qkk6dRTT9V7770nSerXr1+jqzbGxcXptNNOC9YtAUCLGI4CAF2M1+tVZmamcnNzVV5eroiICCUnJysjI0NxcXHBLq+B7du3a+3atTrhhBNkjNELL7ygV1991R+6N27cqL1799Y7JiwsTBkZGerVq5fGjx9fr5d6/vz5Cg8PV0xMjA477DCnbwcAAo6ecADoZPLz85WUlCSfzyefz+ff7nK55HK5lJeXp8TEREdqKS8vb/Thxv2/X3rpJY0cOVKPPPKIMjIyVFJSoqioKN1999169tlnG+3B3v978ODBCg+nLwhA99VcTzghHAA6Ea/Xq/j4eFVUVDTZxu12q6CgoF094jU1NSouLpbb7VZERIS+++47PfXUU7r66qs1fPhwLVq0SDNnztSuXbsaHNu/f3//LCGPPvqoxowZI6/Xq7Vr1+rUU09V7969D7kuAOhOGI7SRXS1r58BdLzMzMx6vd+N8fl8ysrK0rx585pss3v3bq1cubLJKfqKiork8/n0zDPP6LLLLtPWrVt1//3365RTTtHw4cM1atQoXXHFFQ2m5xs6dKjcbneD68XFxfH3FAC0AT3hnURn+voZQPB4PB6VlZW12M7tdmv+/PkaNWqUTj75ZJWUlOiMM85Qenq6kpOT9eWXX+rYY4/1t+/Tp0+jc16fccYZGjt2rKqrq2WtZXgIAHQgesI7Oa/Xq6SkpEa/ft4fypOSktr99TOAzm3Lli2tCuCSVFFRoSuvvFKzZs3SySefLI/Ho8GDBysiIkKSNHr0aOXn5/vDdr9+/WSMafJ8PXr06JB7AAC0DiG8E+ior58BdE579+7Vxo0bG53zesSIEfrTn/4kSTruuOMUHh6uqqqqFs+5f1z40KFDJUnh4eFasmRJvf2TJ08OzA0BANqNEN4J5ObmtiqE5+TkEMKBTsRaq4qKCv8Uem+++aaKi4t12WWXSZIuvPBC/fvf/9a2bdsaHOt2uxUbG6t+/fr5tz3yyCN67rnntGTJkmb/TnC5XLryyiv5ZgwAurCQGhNujJkqaeqoUaNmrFu3Ltjl+IWFhak1n0NYWJiqq6sdqAiAJBUXF2v9+vWN9mDvf+1yuVRSUiJJuvjii7VixQqtXbtWknTrrbequLi40bHYkZGRjQ4PcWp2FABA4DFF4UE624OZrX0Qy+PxqLS01IGKgO6tsrKy3iwh559/vvr06aOcnBxlZ2frvffekzFGV155pZ5++mn/ceHh4Ro6dGiDOa9vuOEGGWO0Y8cOud1u9erVq1318aA2AHQPPJjZySUnJys7O7vFr59TUlIcrAroeqy12rlzpyIiIuRyubRixQq98cYbDRaZ2b59e73j1qxZozFjxkiq/X9tz549crvdmjVrls4//3x/4B44cKDCwsKavH5UVFSH3EdiYqIKCgqUlZWlnJwc/5SlKSkpSk9PpwccALoBesI7Ab5+BlpWXV2tLVu2NDos5LrrrlNCQoJeeeUVXXDBBVqxYoUmTJighQsXaubMmRowYECTqzbGxsYqLi5OPXv2DPYtAgC6GXrCO7m4uDjl5eW1+PUzARzdlbVWxhiVl5fr9ddf13HHHafRo0dr5cqVmjVrloqKirRp06YGz0S4XC4NHTpUF198sSRpwoQJyszM1KBBgyRJl112mS6//PJ2Dw8BAKCjEcI7Cb5+RndkrdWOHTvqDQVp7OHG6667TrfffrvKy8t16aWXav78+Ro9erTcbrc8Ho/GjRtXr/d6/+vo6Oh6w0NGjBihG264wf+e5dMBAJ0Vw1EAHJKqqipt3rzZP0PIxIkTJUlXXXWVfvGLX2jGjBkqLS2tNwWfJBljNHDgwHqB+pxzztHZZ5+tmpoaffPNNxo+fHijS6MDANCVMBwFQJtUVVX5ly9/6aWX5PV6G/Rgb968WTU1NZKkyZMnKz8/X5L07bffauTIkZJqZ/R57LHHNGTIEH/v9ZAhQ+RyuRq9blhYmMaNG+fAHQIAEFz0hAMhxFqr7du3NwjUNTU1uueeeyRJZ599tnbv3q33339fkvSTn/xEq1evVmRkZKNDQmJiYhQXF0d4BgDgIPSEAyFk3bp1Wrt2raZMmSJJ+tOf/qQlS5b4A/fevXvrtTfG6KijjvKH8F//+tfat2+ff//bb7+tfv36KSIiwrmbAACgmyOEA+3g9XqVmZmp3Nxc/8O0ycnJysjI6NCHacvLy+stLtPYA45ff/21IiMj9cQTT+iRRx5RZWWlwsLCtHPnTllr9fOf/7xBD3ZMTEyD4SFXXnllvWvHxsZ22H0AAIBaDEcBDlFHrGporVVxcbEKCwsVFxcnj8ejzz77TAsWLNDDDz+sww8/XA888ID+8Ic/NDg2KiqqXqh+8MEHFR0drQ0bNmjnzp0aP358swvLAACAwGLZ+oMQwtFebVlgKSIiQh988EGDVRv392TvH/rx1ltv6ayzztLixYs1a9Ysvfvuuxo3bpw++eQTvf/++w0WmWH2EAAAOjdC+EEI4Wivq6++Ws8884yqqqqabNOjRw/NmjVL5513ns4880xJtfNWN7Vq48knn6yBAwc6dQsAACDACOEHIYSjOTU1Nfryyy/Vr18/jRw5Utu3b9eNN95Yrwd7165drTqXx+NRYWGhvv/+e8XGxioqKkrGmADfAQAA6AwI4QchhIemffv2aePGjU2u2njSSSfpoYcekrVWffr00Zw5c/Twww+rrKxMRx99dL2e67lz57bqmmFhYQ2WWgcAAKGBKQrR7e3atUs7d+7UsGHDJEkLFiyQy+XS9OnTJUljxozR2rVrGxzndrv9Dzf2799fUu2Ufa+++qpGjRolSerbt69+/PHHesc99dRTKisra7EupvUDAACNIYSjS1i9erXWrVvX5BR95eXlGjt2rL7++mtJ0gsvvCC32+0P4ZdcconCwsIaTM/Xr1+/RoeHTJ48udl6kpOTlZ2dXW9WlIO5XC6lpKS0464BAEB3xXAUBM3evXvrDQ/ZtGmTrr/+ehljdPfdd+vtt9/WRx99JEmaMmWK3nzzTUm1DzwOHTq0QaA+8sgjdeGFF0qSqqur1aNHj4DV3pbZUTpyvnAAANB1MBylG3NqsZi2Xnf37t1av369xo4dqx49eujNN9/Ua6+9Vq/3uri4uMF5r7jiCv/812PHjvVvv++++3TXXXcpJiZGAwcObDFgBzKAS1JcXJzy8vJanCecAA4AABpDT3gX1hGLxbRVdXW1Fi1apOnTp6uqqqreQ4fGGPXq1Usvv/yyNmzYoNmzZ6uwsFAxMTG69957NXfu3EZXbDzwdWRkZJeaPcTr9SorK0s5OTn+f4ykpKQoPT2dAA4AQIhjdpSDdIcQHojhEJWVlf6ZQTZu3KjnnntOSUlJGjlypJYsWaLZs2dr48aNLc724Xa7tXjxYm3dulVTpkxR3759Za3tUuEaAACgvRiO0g1lZmY2+1CgJPl8PmVlZWnevHnat2+f1q5d2+iKjftfb9++XX/96181a9Ysbd26Vb/73e8UFxenkSNHasiQITr11FP11VdfaeXKlaqpqWn2ui+//LLmzZvn30YABwAA+H/oCe+iPB5Pq6bI6927t/bs2aN169bpqKOOqrdv4MCBDVZtTExM1MSJE1VVVaWKigp5PJ5Duq7H41FpaWnbbgoAAKAboSe8i9qzZ48qKip0+OGHy1qrP//5zyosLFRhYWGrgrBUOwOJJA0bNkyLFi3yB+4hQ4aoV69eTR4XHh7eIIBLUnl5eauu29p2AAAAoYgQHgTWWpWUlDQ6LCQ6OloPPvigJGnChAkaP368XnrpJRlj9NBDD8nn8yk2NlY9evRo1UqMffv2lST16tVLF110Ubtrj4iIYJEaAACAdiKEOyg1NVVLly5VYWGhKisr6+0zxmjQoEE68cQT/dtuu+02/yqOkvTjjz+qT58+kqS0tLSgLBbDIjUAAADtx5hwB918883asGFDo1P0DRkyRC6Xq9XnCtZiMSxSAwAA0DqMCe8k9g8z6QjBWiyGRWoAAADaLyzYBeDQJSYmqqCgQKmpqfJ4PAoLC5PH41FqaqoKCgo6fKGeYF8XAACgu2A4CgAAABAAzQ1HoSccAAAAcBghHAAAAHAYIRwAAABwGCEcAAAAcBghHAAAAHAYIRwAAABwGCEcAAAAcBghHAAAAHAYIRwAAABwGCEcAAAAcBghHAAAAHAYIRwAAABwGCEcAAAAcBghHAAAAHAYIRwAAABwGCEcAAAAcJix1ga7BscZY7ZJ2hCES0dLKg7CddE+fG5dE59b18Nn1jXxuXVNfG7OGG6tHdDYjpAM4cFijFlmrU0Idh1oGz63ronPrevhM+ua+Ny6Jj634GM4CgAAAOAwQjgAAADgMEK4sxYGuwAcEj63ronPrevhM+ua+Ny6Jj63IGNMOAAAAOAwesIBAAAAhxHCHWKMmWyM+cYY860x5uZg14PmGWOOMMYsNcZ8bYxZZYy5Ltg1ofWMMT2MMV8YY94Idi1oHWNMP2NMnjFmTd3/d78Idk1onjEmve7vx6+MMX83xvQOdk1oyBjzpDFmqzHmqwO29TfGvGOMWVf3OyqYNYYqQrgDjDE9JM2XlCjpaEmXGGOODm5VaEGVpAxr7ThJJ0i6hs+sS7lO0tfBLgJt8pikt6y1YyUdIz6/Ts0YEyPpWkkJ1tqfSuoh6eLgVoUmPC1p8kHbbpb0/1lrR0v6/+rew2GEcGccL+lba+131tp9khZJOi/INaEZ1tpN1toVda/LVBsIYoJbFVrDGBMraYqk7GDXgtYxxngkTZL0hCRZa/dZa3cGtSi0RrikPsaYcEluSRuDXA8aYa39QFLJQZvPk/RM3etnJJ3vZE2oRQh3RoykHw94XygCXZdhjBkhaYKkT4NcClrnUUm/k1QT5DrQekdK2ibpqbphRNnGmMOCXRSaZq0tkvRnST9I2iSp1Fr7z+BWhTYYZK3dJNV2OkkaGOR6QhIh3BmmkW1MS9MFGGMiJP1D0vXW2l3BrgfNM8acI2mrtXZ5sGtBm4RLmijpr9baCZJ2i6/HO7W6McTnSRopaaikw4wxycGtCuhaCOHOKJR0xAHvY8XXdp2eMcal2gD+nLX25WDXg1Y5SdK5xpj1qh329T/GmNzgloRWKJRUaK3d/21TnmpDOTqv0yV9b63dZq31SXpZ0olBrgmtt8UYM0SS6n5vDXI9IYkQ7ozPJY02xow0xvRU7cMrrwe5JjTDGGNUOz71a2vtI8GuB61jrb3FWhtrrR2h2v/P/mWtpXeuk7PWbpb0ozFmTN2m0yStDmJJaNkPkk4wxrjr/r48TTxM25W8LunyuteXS3otiLWErPBgFxAKrLVVxpjfSnpbtU+QP2mtXRXkstC8kySlSPqvMWZl3bY/WGvfDF5JQLc2R9JzdR0V30m6Msj1oBnW2k+NMXmSVqh2NqkvxAqMnZIx5u+SfiUp2hhTKOkOSQ9KetEYM121/6D6dfAqDF2smAkAAAA4jOEoAAAAgMMI4QAAAIDDCOEAAACAwwjhAAAAgMMI4QAAAIDDCOEAAACAwwjhAAAAgMMI4QAQgowxVxpjrm1m/3xjTFHdaoitOgYA0HqEcAAITX+SdEZjO+qC93mSXrP1V3Rr8hgAQNsQwgEgxBhjRkmKlvSfJpocJylG0qttOAYA0AaEcAAIIcaYVyWtq3t7rzHG1v3cc0CzaZJKJS1twzEAgDYID3YBAABHLZTUQ9I5kmZLKq/b/skBbaZJWmKt9bXhGABAG5j6w/0AAN2dMeY1Sb+w1g5sZN84Sasl/dpam9eaYwAAbcdwFAAIPRMlfdHEvvMl7ZX0VmuPMcZ8Zow5q8OqA4AQQAgHgBBijImWFCtpRRNNpkl6x1pb3tpjrLXHW2vf7uhaAaA7I4QDQGj5Wd3vBoHaGBMrKUEHzIrS0jEAgENDCAeA0DKh7ndjgfp8SVbS6609xhgz1RhTcMD7M40xHxhjthtjthpjbuuAmgGg22F2FAAILUfW/f6hkX3TJH1ord3WhmP8Y8WNMZdJekDSFaqd3vBwSaPaWS8AdEuEcAAILd/V/Z5rjPlEUrWk5yX1kzRJ0u9ae0zdapoTJS01xngkzZV0kbX2nbr2W+p+AAAHYYpCAAghxhi3pAWSElW7AuYP1trhdb3Yz0g60lr7fWuOqdv3g6QUSVGSHrbWjnbsZgCgCyOEAwBkjHlF0khr7bFtOCZa0lbVBvAkSTOttccHpkIA6F54MBMAINWufvmHNh4zUdL31tpS1T60eYwx5jRTa7Ax5pcdXiUAdBOEcACArLV/sta+2cbDJqjuoUxr7ReSrlPtEvflkv4jKa5DiwSAboThKAAAAIDD6AkHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABxGCAcAAAAcRggHAAAAHEYIBwAAABz2/wOe0Gx09+3hVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogy(t/period, wrms, 'ko', label=r'$w_{rms}$',ms=10)\n",
    "ax.semilogy(t/period, np.exp(log_w0)*np.exp(gamma_w*t), 'k-.', label='$\\gamma_w = %f$' % gamma_w)\n",
    "ax.legend(loc='upper right',fontsize=18).draw_frame(False)\n",
    "ax.set_xlabel(r\"$t/t_{ic}$\",fontsize=18)\n",
    "ax.set_ylabel(r\"$w_{rms}$\",fontsize=18)\n",
    "\n",
    "fig.savefig('growth_rates.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:dedalus]",
   "language": "python",
   "name": "conda-env-dedalus-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
